QUICKSTART.md

🚀 RAG System Quick Start

This quick guide will help you launch the RAG system in 5 minutes!

Prerequisites

✅ Python 3.9+
✅ Ollama installed and running
✅ llama3.2 model downloaded

Step 1: Check Ollama

# Check that Ollama is installed
ollama --version

# Check available models
ollama list

# If llama3.2 is not in the list, download it
ollama pull llama3.2

Step 2: Activate Virtual Environment

cd /Users/v.hirenko/Desktop/DevHubVault/my-ai-projects/rag-python-rag
source venv/bin/activate

Step 3: Run the Application

python main.py

What Will Happen?

1. ⬇️ Test document will be downloaded (Think Python PDF)
2. 📄 Document will be converted to markdown
3. ✂️ Text will be split into 847 chunks
4. 🔢 Embeddings will be generated for each chunk
5. 💾 Data will be saved to ChromaDB
6. 🌐 Web interface will open at http://localhost:7860

Usage Example

After launching, open your browser and go to http://localhost:7860

Try these questions:

In English:

• "How do if-else statements work in Python?"
• "What are the different types of loops in Python?"
• "How do you handle errors in Python?"

In other languages:

• "Як працюють умовні оператори if-else в Python?" (Ukrainian)
• "Какие типы циклов есть в Python?" (Russian)
• "Як обробляти помилки в Python?" (Ukrainian)

Execution Time

⏱️ First run: ~1-2 minutes
⏱️ Subsequent runs: ~5-10 seconds
⏱️ Answer to question: ~5-15 seconds

Troubleshooting

❌ "Model llama3.2 not found"

ollama pull llama3.2

❌ "Connection refused to localhost:11434"

# Make sure Ollama is running
ollama serve

❌ "No module named 'fitz'"

source venv/bin/activate
pip install -r requirements.txt

Next Steps

✅ Done? Great! Now try:

1. Add your own documents:

   • Place PDF/DOCX files in the documents/ folder
   • Restart the application

2. Configure parameters:

   • Open config.py
   • Change model, chunk size, and other parameters

3. Use programmatically:

   from vector_store import retrieve_context
   from llm_handler import generate_answer
   
   question = "Your question here"
   context, sources = retrieve_context(question)
   answer = generate_answer(question, context)
   print(answer)
   

Useful Commands

# Check component status
python vector_store.py      # Vector DB statistics
python llm_handler.py        # LLM test
python document_converter.py # Document conversion

# Clear and reindex
python -c "
from vector_store import VectorStore
vs = VectorStore()
vs.clear_collection()
"

# Then restart main.py
python main.py

Need Help?

📖 Full documentation: README.md
🐛 Found a bug? Create an Issue
💡 Have ideas? Pull Requests are welcome!

---

Enjoy using the system! 🎉